Sodium-glucose cotransporter-2 inhibitors are the most recently approved class of antidiabetic drugs. These drugs have an attractive efficacy profile ? including a decreased risk of major adverse cardiovascular events in addition to glucose-lowering, weight loss, and blood pressure-lowering. However, SGLT2 inhibitors also have significant undesired side effects ? including bone loss as well as increased risk of bone fractures, urosepsis, and ketoacidosis. Significant variability exists in response to these drugs in terms of both efficacy and safety, and there are not currently good ways to identify individuals likely to respond or experience side effects. This application proposes a genome-wide association study in the Old Order Amish population to identify genetic variants that predict individuals' responses to canagliflozin (the most widely used SGLT2 inhibitor) at a dose of 300 mg/day for 5 days. Based on preliminary data from the Principal Investigators' research as well as information from the literature, the proposed project will measure pharmacodynamic end-points related to both the beneficial and adverse effects of SGLT2 inhibitors. The proposal contains two specific aims: Aim 1. To identify variants associated with a clinical efficacy biomarker (24 hr urinary glucose excretion) Aim 2. To identify variants associated with predictive biomarkers for safety end-points ? including, plasma glucagon, ketone bodies, cardiovascular biomarkers (uric acid and blood pressure) and biomarkers of bone health (serum phosphorus, plasma FGF23, plasma 1,25-dihydroxyvitamin D, and plasma PTH). Genotyping will be conducted using a high-density array with comprehensive coverage of DNA sequence variants. The project will leverage an Amish-specific imputation panel generated from whole genome sequence data on ~1100 Amish individuals obtained through the NHLBI-sponsored Trans-Omics for Precision Medicine (TOPMed) program. Based on the history of previous studies, genetic data obtained in the Old Order Amish population have been highly predictive of observations in the general population and relevant patient populations. Based on these precedents, we anticipate that genetic variants in this study are very likely to be predictive of clinical responses of SGLT2 inhibitor-treated type 2 diabetic patients. The proposed study is a step toward the long-term objective of identifying genetic biomarkers to predict an individual patient's response to SGLT2 inhibitors. Availability of predictive biomarkers would enable physicians to prescribe optimal therapies for each individual patient based on predictors of beneficial response and susceptibility to adverse effects. This type of Precision Medicine approach, based on predictive pharmacogenomic biomarkers, would be a transformational advance in the way diabetes drugs are prescribed.